Questions tagged [vgg]

For questions related to the VGG neural networks, which were proposed in "Very Deep Convolutional Networks for Large-Scale Image Recognition" (2015) by Karen Simonyan and Andrew Zisserman.

Filter by
Sorted by
Tagged with
-1 votes
1 answer
69 views

Denoise autoencoder not training properly [closed]

I'm trying to make a denoise autoencoder wherein the encoder part is vgg16 and decoder is opposite of vgg16(encoder) network. My dataset consists of 5K images in grayscale. Now while training, the ...
user avatar
1 vote
1 answer
58 views

How does a VGG-based Style-Loss incorporate color information?

I've recently been reading a lot about style transfer, its applications and implications. I understand what the Gram matrix is and does. I can program it. But one thing that has been boggling me is: ...
user avatar
0 votes
1 answer
81 views

Why the partial derivative is $0$ when $F_{ij}^l < 0$?. Math behind style transfer

I am currently in the process of reading and understanding the process of style transfer. I came across this equation in the research paper which went like - For context, here is the paragraph - ...
user avatar
1 vote
2 answers
273 views

Does replacing 3x3 filters with 3x1 and 1x3 filters improve the performance?

Recently I have come up with a VGG16 model for my binary classification task. I have relatively simple signal images Therefore (maybe?) other deeper models like ...
user avatar
0 votes
0 answers
23 views

Strategy to input and get large images in VGG neural networks

I'm using a transfert-style based deep learning approach that use VGG (neural network). The latter works well with images of small size (512x512pixels), however it provides distorted results when ...
user avatar
1 vote
3 answers
1k views

Is a VGG-based CNN model sometimes better for image classfication than a modern architecture?

I have an image classification task to solve, but based on quite simple/good terms: There are only two classes (either good or not good) The images always show the same kind of piece (either with or ...
user avatar
  • 155
5 votes
2 answers
155 views

How do I improve accuracy and know when to stop training?

I am training a modified VGG-16 to classify crowd density (empty, low, moderate, high). 2 dropout layers were added at the end on the network each one after one of the last 2 FC layers. network ...
user avatar
  • 115
0 votes
3 answers
376 views

How are the dimensions of the feature maps produced by the convolutional layer determined in VGG-16?

I'm trying to understand how the dimensions of the feature maps produced by the convolution are determined in a ConvNet. Let's take, for instance, the VGG-16 architecture. How do I get from 224x224x3 ...
user avatar
2 votes
1 answer
332 views

Why does the number of feature maps increases in the VGG model?

I found the below image of how a CNN works But I don't really understand it. I think I do understand CNNs, but I find this diagram very confusing. My simplified understanding: Features are ...
user avatar
4 votes
1 answer
1k views

Trying to understand VGG convolution neural networks architecture

Trying to understand the VGG architecture and I have these following questions. I understand the general understanding of increasing filter size is because we are using max pooling and so its image ...
user avatar